The design of modern and future wireless radio communication technologies requires radio interfaces that are characterized by high bandwidth efficiency and flexibility. Advanced signal processing, especially when combined with the use of multiple antennas, is commonly understood to enhance the performance in terms of coverage, capacity and/or quality of service. Knowledge of the radio channel is of vital importance in any wireless communication system, e.g. for channel modeling in radio link and system simulations, channel prediction and possible link adaptation.
An advanced approach for channel estimation is based on discrete wave channel modeling involving a number of discrete waves, combined with Maximum Likelihood (ML) parameter estimation. The main use of discrete wave channel estimation and modeling of the multiple channels is to accurately characterize the channels from channel measurements and to use this characterization for channel modeling in radio link and system simulations. Extensive efforts have been put on measurement campaigns and characterization of the radio channel to provide input to 3GPP (3rd Generation Partnership Project), COST (COperation européenne dans le domaine de la recherche Scientifique et Technqiue) 259, COST 273, COST 2100, ITU (International Telecommunication Union), ETSI (European Telecommunications Standards Institute) and other channel modeling efforts. Another use of highly resolved channel estimation is improved channel prediction, which in turn can be used for enhanced link adaptation including controlling coding and/or transmit power.
It is well known that Maximum Likelihood in principle is one of the most accurate methods available. It suffers however from considerable computational complexity. The basic problem is that maximization is not feasible with respect to all waves and the corresponding parameters simultaneously. Different methods have previously been proposed for complexity reduction like Space Alternating Generalized Expectation (SAGE) maximization, and a maximum likelihood framework called RIMAX [1]. The problem with SAGE is that the convergence is very slow when the estimated parameters are dependent on each other. This problem is to a large extent solved with RIMAX which uses a gradient based method to find a local maximum. The problem with RIMAX is however that all parameters in principle have to be estimated simultaneously. Though the convergence is much faster it is still not feasible to handle the number of waves which in most practical cases may be several hundreds. For RIMAX this is solved by identifying groups of uncoupled waves and performing sequential maximization for parameters within these groups. It is however difficult, and often not even possible, to find uncoupled groups of waves, i.e. groups of waves with no or low correlation and/or with no or low power overlap.
There is thus a general need for improved techniques for channel estimation.